I'm struggling with a space-time problem. Help is very much appreciated! Here's first what I'm looking for:
I've got a dataframe with fixes of roe deer datapoints (x,y-coordinates) taken in a irregular approximatly five minute interval.
I want to find all fixes within a certain radius (let's say 15 Meters) of the acutall position of the roe deer, with the condition that they are consecutiv for about 30 Minutes (so six fixes). Oviously the number of fixes can vary along the trajectory. I want to write down the number of fixes into a new column and want to mark in another column (0=no,1=yes), if the condition is met.
If the conditions are met, I would like to calculate the center of gravity (in x,y-coordinates) of said pointcloud and write it to this or another dataframe.
According to another users question (Listing number of obervations by location) I was able to find the fixes within the radius, but i couldn't figure a way how to ensure that they are consecutive
Here's some rows of my dataframe (in realty there's more than 10000 rows, because i subsetted this data.frame the ID's are not starting with one):
FID No CollarID Date Time Latitude__ Longitude_ Height__m_ DOP FixType
1 0 667 7024 2013-10-22 06:01:49 47.26859 8.570701 609.94 10.6 GPS-3D
2 1 668 7024 2013-10-22 06:06:04 47.26861 8.570634 612.31 10.4 GPS-3D
3 2 669 7024 2013-10-22 06:11:07 47.26871 8.570402 609.43 9.8 GPS-3D
4 3 670 7024 2013-10-22 06:16:14 47.26857 8.570796 665.40 4.4 val. GPS-3D
5 4 671 7024 2013-10-22 06:20:36 47.26855 8.570582 653.65 4.6 val. GPS-3D
6 5 672 7024 2013-10-22 06:25:50 47.26850 8.570834 659.03 4.8 val. GPS-3D
7 6 673 7024 2013-10-23 06:00:53 47.27017 8.569882 654.86 3.6 val. GPS-3D
8 7 700 7024 2013-10-26 12:00:18 47.26904 8.569596 651.88 3.8 val. GPS-3D
9 8 701 7024 2013-10-26 12:05:41 47.26899 8.569640 652.76 3.8 val. GPS-3D
10 9 702 7024 2013-10-26 12:10:40 47.26898 8.569534 650.42 4.6 val. GPS-3D
11 10 703 7024 2013-10-26 12:16:17 47.26896 8.569606 653.77 11.4 GPS-3D
12 11 704 7024 2013-10-26 12:20:18 47.26903 8.569792 702.49 9.8 val. GPS-3D
13 12 705 7024 2013-10-26 12:25:47 47.26901 8.569579 670.12 2.4 val. GPS-3D
14 13 706 7024 2013-10-26 12:30:18 47.26900 8.569477 685.65 2.0 val. GPS-3D
15 14 707 7024 2013-10-26 12:35:23 47.26885 8.569400 685.15 6.2 val. GPS-3D
Temp___C_ X Y ID Trajectory distance speed timelag timevalid
1 19 685667.7 235916.0 RE01 RE01 5.420858 0.021258268 4.250000 1
2 20 685662.6 235917.8 RE01 RE01 21.276251 0.070218649 5.050000 1
3 20 685644.9 235929.5 RE01 RE01 34.070730 0.110979577 5.116667 1
4 20 685675.0 235913.5 RE01 RE01 16.335573 0.062349516 4.366667 1
5 20 685658.8 235911.3 RE01 RE01 19.896906 0.063365941 5.233333 1
6 20 685677.9 235905.7 RE01 RE01 199.248728 0.002346781 1415.050000 0
7 22 685603.2 236090.4 RE01 RE01 126.831124 0.000451734 4679.416667 0
8 22 685583.4 235965.1 RE01 RE01 6.330467 0.019598970 5.383333 1
9 22 685586.8 235959.8 RE01 RE01 8.270701 0.027661208 4.983333 1
10 23 685578.8 235957.8 RE01 RE01 5.888147 0.017472246 5.616667 1
11 22 685584.3 235955.7 RE01 RE01 16.040998 0.066560158 4.016667 1
12 23 685598.3 235963.6 RE01 RE01 16.205330 0.049256322 5.483333 1
13 23 685582.2 235961.6 RE01 RE01 7.742184 0.028568946 4.516667 1
14 23 685574.5 235960.9 RE01 RE01 18.129019 0.059439406 5.083333 1
15 23 685568.8 235943.7 RE01 RE01 15.760165 0.051672673 5.083333 1
Date_text Time_text DateTime Flucht FluchtALL
1 22.10.2013 06:01:49 22.10.2013 06:01:49 0 0
2 22.10.2013 06:06:04 22.10.2013 06:06:04 0 0
3 22.10.2013 06:11:07 22.10.2013 06:11:07 0 0
4 22.10.2013 06:16:14 22.10.2013 06:16:14 0 0
5 22.10.2013 06:20:36 22.10.2013 06:20:36 0 0
6 22.10.2013 06:25:50 22.10.2013 06:25:50 0 0
7 23.10.2013 06:00:53 23.10.2013 06:00:53 0 0
8 26.10.2013 12:00:18 26.10.2013 12:00:18 0 0
9 26.10.2013 12:05:41 26.10.2013 12:05:41 0 0
10 26.10.2013 12:10:40 26.10.2013 12:10:40 0 0
11 26.10.2013 12:16:17 26.10.2013 12:16:17 0 0
12 26.10.2013 12:20:18 26.10.2013 12:20:18 0 0
13 26.10.2013 12:25:47 26.10.2013 12:25:47 0 0
14 26.10.2013 12:30:18 26.10.2013 12:30:18 0 0
15 26.10.2013 12:35:23 26.10.2013 12:35:23 0 0
and here's the code i've got so far:
for (i in seq(nrow(df)))
{
# circle's centre
xcentre <- df[i,'X']
ycentre <- df[i,'Y']
# checking how many fixes lie within 15 m of the above centre, noofcloserest column will contain this value
df[i,'noofclosepoints'] <- sum(
(df[,'X'] - xcentre)^2 +
(df[,'Y'] - ycentre)^2
<= 15^2
) - 1
cat(i,': ')
# this prints the true/false vector for which row is within the radius, and which row isn't
cat((df[,'X'] - xcentre)^2 +
(df[,'Y'] - ycentre)^2
<= 15^2)
cat('\n')
}
I tried to test the consecutive-condition on the TRUE/FALSE List from cat(), but i couldn't access the results to process them any further.
I know this is a mile long question, I would be very glad if someone could help me with this or part of this problem. I will be thankful till the end of my life :). Btw: you may have noticed, that I'm an unfortunate R beginner. Many thanks!
Here's how
You can apply a rolling function to your time series for a window of 6.
library(xts)
## You read you time serie using the handy `read.zoo`
## Note the use of the index here
dx <-
read.zoo(text="Date Time DOP X Y noofclosepoints
4705 09.07.2014 11:05:33 3.4 686926.8 231039.3 14
4706 09.07.2014 11:10:53 3.2 686930.5 231042.5 14
4707 09.07.2014 11:16:29 15.8 686935.2 231035.5 14
4708 09.07.2014 11:20:08 5.2 686932.9 231035.6 14
4709 09.07.2014 11:25:17 4.8 686933.8 231038.6 14
4710 09.07.2014 11:30:16 2.2 686938.0 231037.0 15
4711 09.07.2014 11:35:13 2.0 686930.9 231035.8 14
4712 09.07.2014 11:40:09 2.0 686930.6 231035.7 14
4713 09.07.2014 11:45:25 3.4 686907.2 231046.8 0
4714 09.07.2014 11:50:25 3.2 686936.1 231037.1 14",
index = 1:2,format="%d.%m.%Y %H:%M:%S",tz="")
## You apply your rolling to all columns
## for each window you compute the distance between the current
## point and others , then you return the number of points within the radius
as.xts(rollapplyr(dx,7,function(x) {
curr <- tail(x,1)
others <- head(x,-1)
dist <- sqrt((others[,"X"]-curr[,"X"])^2 + (others[,"Y"]-curr[,"Y"])^2 )
sum(dist<15)
},by.column=FALSE))
# [,1]
# 2014-07-09 11:35:13 6
# 2014-07-09 11:40:09 6
# 2014-07-09 11:45:25 0
# 2014-07-09 11:50:25 5